Which companies benefit from an AI agent?

If a task repeats often, follows clear rules and is still done manually, it is a good candidate. Lead qualification, ticket triage or data maintenance in the CRM are typical starting points. For pure one-off cases without repetition, the effort rarely pays off.

Does the agent make decisions without control?

No. You decide what the agent may do on its own and where a human confirms. Sensitive or expensive actions only run after approval. Every step is logged, so it stays traceable what the agent did and why.

What about data protection and GDPR?

We keep the agent's access as tight as possible and document it. You decide which data gets processed. On request, the orchestration runs through n8n on an EU server, so processing stays in the EU.

What technology do the agents run on?

We orchestrate the logic with n8n, connected to HubSpot and your internal systems. As the model we use the one that fits the task. You are not locked into a single vendor and keep the workflows in your own hands.

How do we get started?

With an intro call where we look for a fitting first use case. From that we build a clearly scoped agent for one process, not a big project for everything at once.

AI agents

AI agents that do real work

We build AI agents that work inside your CRM and automation processes. They read requests, decide on the next step and carry it out in HubSpot and n8n. With guardrails that you define.

3 areas

sales, service, back office

CRM-native

works in HubSpot & n8n

EU hosting

GDPR-compliant on request

Free intro callno strings, about 30 minutes

live

Triggernew request

AI agentdecides

HubSpotcontact & deal

Task✓ done

Between a chatbot and real relief

Many teams tried a chatbot and noticed: it answers questions, but the work stays on the pile. An AI agent sits one level higher. It gets a task, gathers the information it needs and runs the steps someone would otherwise do by hand.

For that to work reliably, two things matter: access to the right systems and clear limits. That is exactly where we start. The agent runs inside your CRM and your workflows, not in a disconnected playground.

Where AI agents work in the company

Three areas where the entry usually pays off quickly. Which one fits you first, we figure out in the call.

Sales

New inquiries get qualified, routed to the right colleague and prepared with a first reply draft. The agent researches the company, fills missing deal fields and creates the next task.

Qualify and route leads
Prepare reply drafts
Fill deal fields automatically

Customer service

Incoming tickets are read, categorized and answered where the answer is clear. Everything else goes to a human with a summary and a suggested reply.

Read and categorize tickets
Answer standard cases directly
Escalate complex cases with context

Back office

Data from emails, PDFs and forms ends up structured in the CRM. The agent checks for duplicates, normalizes spellings and triggers follow-up steps.

Extract data from documents
Maintain and check master data
Trigger routine tasks

How we build the agents

Not a demo that only runs in show mode. An agent connected to your systems that takes on responsibility within clear limits.

Agents inside your HubSpot workflow

The agent hooks into your existing HubSpot processes. It reads contacts and deals, writes back and works with the same pipelines as your team.

Orchestrated with n8n

n8n connects trigger, model and target systems. Every step is visible, can be tested and logs what the agent did.

Connected to your data

The agent accesses the sources it needs for a good decision: CRM, knowledge base, internal APIs. Read-only where reading is enough.

With guardrails and approval

You decide what the agent may do on its own and where a human confirms. Sensitive actions only run after approval. On request, processing stays in the EU.

Chatbot, automation or agent?

The three often get lumped together. The difference is how much decision sits inside the system.

ChatbotClassic automationAI agent

Understanding inputReplies to chat messagesReacts to fixed triggersUnderstands requests from mail, form, CRM

DecisionFollows a dialog treeIf-then rulesPicks the next step itself, within the set frame

ToolsUsually nonePredefined actionsCalls CRM, APIs and search as needed

Multiple stepsOne replyFixed chainPlans across several steps to the result

Edge cases"I did not understand that"Stops or ignoresHands over to a human with a summary

How we work

From the first use case to an agent that acts on its own within a safe frame.

Sharpen the use case

We look for the process where an agent shows impact early: repeated often, clear enough for rules, annoying enough that it gets in the way.

Connect data and tools

We clarify what the agent needs and may access. CRM, inbox, internal APIs. Access stays tight and documented.

Build and frame the agent

Trigger, model, tools and guardrails. The agent only gets the actions it really needs, with clear limits.

Test in the shadow

First the agent runs along without acting. We compare its suggestions with real decisions and adjust.

Go live step by step

First suggestions with approval, then automatic actions for the safe cases. You stay in control of the pace.

Watch and refine

Logging and alerts show what is happening. Where the agent is off, we adjust prompt, tools or limits.

Frequently asked questions about AI agents

Short and specific, so you can judge whether an agent fits your process.

A chatbot answers messages in a conversation. An AI agent gets a goal and works toward it: it reads the request, decides on the next step, calls tools like the CRM or an API and runs several steps until the task is done. The chatbot talks, the agent acts.